Group fairness in federated learning
Several recent works have made some progress on group fair FL. One research direction is to design an optimization objective with fairness constraints [1]}, [2]}, which requires each client to share the statistics of sensitive information to the server. Abay et al. also [3]} investigated the effectiveness of adopting centralized debiasing mechanisms on each client. Local debiasing strategies are ineffective under the scenarios in which different clients have the heterogeneous sensitive attribute distribution, which limits its application in real-life scenarios. Compared to existing works, our proposed method does not restrict the local debiasing strategy of each participating client, thus increasing the flexibility of the system. Empirical evaluations also show that our method can yield more fairness improvements under higher data heterogeneity.
